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AI and Machine Learning: Leveraging Technology to Accelerate Clinical Trials

5 Questions with Raj Indupuri, CEO and Co-founder

In a recent interview with TechChannel, eClinical’s CEO and co-founder Raj Indupuri discussed the role of analytics, AI and machine learning in reducing cycle times in clinical studies. Here’s more from Raj on why taking an agile, data-first approach is so critical as trials become more complex.

What makes data such a valuable asset in clinical research and how can organizations tap into its potential? 

The amount of data that the industry has been collecting to conduct trials continues to increase. This has been the case for the past few years. To tap into this data and transform it into valuable insights, big data, advanced analytics, AI and machine learning can play a huge role in reducing overall cycle times. Ensuring that data is cleaned in near real time so that you can do analysis and submit it to the FDA is crucial. Three to five years from now, big data, AI and machine learning will be pervasive across clinical development and the life sciences industry.

How is the role of data managers evolving?

We are now trying to collect data directly from patients, whether it’s with devices or apps. With these new streams of data coming, data management plays a huge role in curating and cleaning this data, as well as ensuring that this data is high quality. This way, statisticians or other stakeholders can analyze it before data is submitted to the FDA.

Data management is extremely important in the value chain. The data management function must also take advantage of advanced analytics and machine learning models so that data managers can do their jobs efficiently and help their organizations scale. Technology is a vital part of this strategy to solve life sciences’ data challenges.

What is the current impact of digital transformation in the life sciences industry?

More than ever, life sciences companies are investing in digital transformation initiatives. That means there is a greater focus on modernizing the technology infrastructure to handle big data, to deploy advanced analytics or to implement machine learning models. There must be alignment across the operation if you want to transform and change. So we are seeing that a lot in terms of this transformation issue. There is a need for improved infrastructure to extract value from all the data that we are now collecting.

What are the business challenges of implementing effective data management strategies? 

We always recommend taking an agile approach, to think about quick wins and not try to boil the ocean. Rather than investing heavily and aggressively to build large data lakes, break down the problem and try to build or deploy data management capabilities in an intelligent way. Instead of building massive data infrastructures, why don’t we solve this in a modular way? For example, you could build a data lake for clinical research data and one for R&D data. The key is to take a platform approach and build an ecosystem where there is profitability. By doing so, data hubs or data lakes can talk to each other rather than having one big infrastructure to solve every use case.

What are the business benefits of adopting AI and machine learning? 

A competitive advantage can only be gained by the right combination of both human intelligence and automation. At the end of the day, it’s augmented intelligence. If you take the example of data managers who conduct data reviews, we’re building machine learning models so that the machine itself can detect data issues. The way we do this is that we actually have to tap into human intelligence and train the machine learning model so that these models can get better and they can deliver. It’s all about people, process and technology. How you bring everything together and have them aligned for those quick wins will get you immediate or quick ROI.

For more about the future trends in big data and the impact of AI and machine learning on clinical studies, read the full interview with Raj on TechChannel.

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